2024-10-23

1. Introduction

Learning Objectives

  • You should be able to critically analyse how data is visualised
  • You should be able to judge a figure’s clarity and potential for misunderstanding
  • You should be able to identify potential sources of bias resulting from the visualisation
  • You should understand how to create effective figures for your own work

Background Reading

Exercise: Four figures (assess all figures)

  • For each figure, consider the following:
    • What type of data is being presented?
    • Are the data presented effectively? (why/why not?)
    • How can the data presentation be improved?
    • Use the DOI provided to find the paper the figure is from, if you need more information than is in the figure legend
  • Fill in the pro forma with your answers to the questions above (one sentence each)

2. Summary Results

Responses by figure

  • We received 1 ratings in total (at three figures per student, this is 0.3 students responding)

Overall effectiveness

  • How was effectiveness scored, distributed across all figures?

Overall understandability

  • How was understandability scored, distributed across all figures?

Overall appeal

  • How was appeal scored, distributed across all figures?

Time taken per figure

  • How long did you take, per figure?

3. Results By Figure

Effectiveness/Understandability/Appeal

  • How effective/understandable/appealing did you think each figure was?

Colours/Fonts/Labels

  • How well did each figure use colours, fonts, and labels?

Statistics/Whitespace/Data

  • How well did each figure use statistics, whitespace, and data?

Reproduction

  • How well did you think you could reproduce each figure?

4. Specific Figures

Figure 1 (doi:10.1073/pnas.2320257121)

Figure 1 (doi:10.1073/pnas.2320257121)

Figure 1 (doi:10.1073/pnas.2320257121)

  • Suggested improvements:
    • The lower extent of error bars is not visible in (B), (D), (G), or (E). Avoid “dynamite plots.”
    • Bar charts should be avoided; 1D scatterplot for (D), (G), and (E) would be clearer. Line plot with concentration on \(x\)-axis would improve (B).
    • We can’t see difference between effects of two concentrations of A16 in (D), or A16 vs A18 in (B); use a table of contrasts.
    • Place things to be compared by the reader next to each other where possible (E).

Figure 1 (doi:10.1073/pnas.2320257121)

improvements
Cover it with gasoline and set it on fire.

Figure 2 (doi:10.1073/pnas.2405474121)

Figure 2 (doi:10.1073/pnas.2405474121)

Figure 2 (doi:10.1073/pnas.2405474121)

  • Suggested improvements:
    • UMAP plots (B, E) are highly manipulable and clustering/placement does not necessarily reflect objective measures.
    • Unpleasant colour choices in (C); there is room for aesthetic improvement.
    • The proportion plot in (C) does not give information on absolute number, only proportion; a proportional areas plot spanning all clusters would more honestly represent the data.
    • Heatmap text is too small to read comfortably; is there too much data here?

Figure 2 (doi:10.1073/pnas.2405474121)

improvements

Figure 3 (doi:10.1073/pnas.2408540121)

Figure 3 (doi:10.1073/pnas.2408540121)

Figure 3 (doi:10.1073/pnas.2408540121)

  • Suggested improvements:
    • The lower extent of error bars is not visible in (D) or (E). These are “dynamite plots,” which should be avoided.
    • Bar charts should be avoided in general; a 1D scatterplot of each dataset in (D) and (E) would be clearer.

Figure 3 (doi:10.1073/pnas.2408540121)

improvements

Figure 4 (doi:10.1073/pnas.240342112)

Figure 4 (doi:10.1073/pnas.240342112)

Figure 4 (doi:10.1073/pnas.240342112)

  • Suggested improvements:
    • The rifampicin structure is purely dewxcorative and could be removed.
    • The lower extent of error bars is not visible in (B). This is a “dynamite plot,” which should be avoided.
    • Bar charts should be avoided in general; a 1D scatterplot of each dataset in (B) would be clearer.
    • The implied membrane in (C) and (D) could be stated as such in the figure.

Figure 4 (doi:10.1073/pnas.240342112)

improvements

5. Summing Up

General Comments

  • Colour choices
  • Larger figures/graphs, more space between figures/graphs
  • Too much data per figure
  • Split into multiple figures
  • Remove unnecessary data (how do we define this?)
  • “The data is presented in a manner that would likely be inaccessible for people without prior experience. A move toward a more palatable/digestible format will facilitate better science communication in the future.”

Visualising Data About Data Visualisation

  • What did you say about figure effectiveness?

Visualising Data About Data Visualisation

  • What words did you use to describe figure improvements?

Data Visualisation is Not Neutral